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Adaptive Biometric Strategy using Doddington Zoo Classification of User's Keystroke Dynamics

Abir Mhenni 1, 2 Estelle Cherrier 3 Christophe Rosenberger 4 Najoua Essoukri Ben Amara 1
3 Equipe Automatique - Laboratoire GREYC - UMR6072
GREYC - Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen
4 Equipe Monétique & Biométrie - Laboratoire GREYC - UMR6072
GREYC - Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen
Abstract : Securing personal, professional and even official data is a very critical issue nowadays, giving that these infor-mations are safeguarded in different devices (mobile, computer) and various accounts (social networks, e-mails). To protect them from unauthorized acess, users generally are asked to use passwords. But using only these authentication solutions is no longer efficient against hacker attacks. Keystroke dynamics is a biometric promising modality that guarantees the recognition of the user's characteristics; his typing manner on the keyboard. Regarding that the typing rythm of the user changes over time, adaptive biometric solutions help to take into consideration these variations. In this paper we classify user into multiple categories according to Doddonghton Zoo classification. Afterwards, we apply an adaptive strategy specific to each category of users. The achived experiments demonstrate that an update strategy specific to the user class has improved significantly the obtained performances.
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https://hal-normandie-univ.archives-ouvertes.fr/hal-01761086
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Submitted on : Tuesday, July 17, 2018 - 4:25:33 PM
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  • HAL Id : hal-01761086, version 1

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Abir Mhenni, Estelle Cherrier, Christophe Rosenberger, Najoua Essoukri Ben Amara. Adaptive Biometric Strategy using Doddington Zoo Classification of User's Keystroke Dynamics. 14th International Wireless Communications and Mobile Computing Conference (IWCMC), Jun 2018, Limassol, Cyprus. ⟨hal-01761086⟩

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